Automatic abstraction for verification of cyber-physical systems
Proceedings of the 1st ACM/IEEE International Conference on Cyber-Physical Systems
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Cyber-physical systems, in which computers control real-world mechanisms, are ever more pervasive in our society. These complex systems, containing a mixture of software, digital hardware, and analog circuitry, are often employed in circumstances where their correct behavior is crucial to the safety of their operators. Therefore, verification of such systems would be of great value. This dissertation introduces a modeling and verification methodology sufficiently powerful to manage the complications inherent in this mixed discipline design space. Labeled hybrid Petri net (LHPNs) are a modeling formalism that has been shown to be useful for modeling analog/mixed signal systems. This dissertation presents an extended LHPN model capable of modeling complex computer systems. Specifically, this extended model uses discrete valued variables to represent software variables. In addition, a rich expression syntax has been added to model the mathematical operations performed in computer processors. No formalism is useful if it remains inaccessible to designers. To facilitate the use of this model, a translation system is presented that enables the compilation of LHPNs from intermediate descriptions similar to assembly language. Users can create an LHPN construction language appropriate to each portion of their design. Once a model is defined, it is necessary to determine the range of behaviors of that system. Specifically, a determination must be made if the model exhibits any behaviors that violate the design constraints. To that end, this dissertation describes an efficient state space exploration method. This method uses state sets to represent the potentially infinite state spaces of LHPN models. Complex models often yield intractably large state spaces, resulting in unacceptably long runtimes and large memory requirements. It is, therefore, often necessary to distill from a model the information necessary to prove a particular property, while removing extraneous data. This dissertation presents a number of correctness preserving transformations that depend on simple, easily checked properties to reduce the complexity of LHPNs. These transformations alleviate the need to model variables, transitions, and places that do not contribute to correctness of the property under test. Finally, an in depth case study is used to demonstrate the utility of this method. Each step in the modeling and analysis process is applied in turn to this example, showing its progression from initial block diagram to final verified implementation.